A list of schools receiving Quality Reviews during the 2017-18 school year

The objective of the Quality and Outcomes Framework (QOF) is to improve the quality of care patients are given by rewarding practices for the quality of care they provide to their patients, based on a number of indicators across a range of key areas of clinical care and public health. This publication provides data for the reporting year 1 April 2018 to 31 March 2019 and covers all General Practices in England that participated in the Quality and Outcomes Framework (QOF) in 2018-19. Participation in QOF is voluntary, though participation rates are very high at 95.1 per cent. 29 October 2019: revised PREVALENCE_1819.csv, ORGANISATION_REFERENCE_1819.csv and INDICATOR_MAPPINGS_1819.csv published. The previous versions included prevalence data for eight GP practices which should have been excluded as a result of the validation process. This issue does not affect any of the other files in this publication. For more details of the validation process, please refer to the technical annex.

This statistic shows the results of a survey conducted in the United States in 2018 on the quality of public transportation in the local area. Some ** percent of respondents state that the public transport system is very well developed within their area. The Survey Data Table for the Statista survey Cars & Mobility in the United States 2018 contains the complete tables for the survey including various column headings.

Dataset of the 'Internet of Things: Online Anomaly Detection for Drinking Water Quality' competition hosted at The Genetic and Evolutionary Computation Conference (GECCO) July 15th-19th 2018, Kyoto, Japan

The task of the competition was to develop an anomaly detection algorithm for a water- and environmental data set.

Included in zenodo:

- dataset of water quality data

- additional material and descriptions provided for the competition

The competition was organized by:

F. Rehbach, M. Rebolledo, S. Moritz, S. Chandrasekaran, T. Bartz-Beielstein (TH Köln)

The dataset was provided by:

Thüringer Fernwasserversorgung and IMProvT research project

GECCO Industrial Challenge: 'Internet of Things: Online Anomaly Detection for Drinking Water Quality'

Description:

For the 7th time in GECCO history, the SPOTSeven Lab is hosting an industrial challenge in cooperation with various industry partners. This years challenge, based on the 2017 challenge, is held in cooperation with "Thüringer Fernwasserversorgung" which provides their real-world data set. The task of this years competition is to develop an anomaly detection algorithm for the water- and environmental data set. Early identification of anomalies in water quality data is a challenging task. It is important to identify true undesirable variations in the water quality. At the same time, false alarm rates have to be very low.
Additionally to the competition, for the first time in GECCO history we are now able to provide the opportunity for all participants to submit 2-page algorithm descriptions for the GECCO Companion. Thus, it is now possible to create publications in a similar procedure to the Late Breaking Abstracts (LBAs) directly through competition participation!

Accepted Competition Entry Abstracts
- Online Anomaly Detection for Drinking Water Quality Using a Multi-objective Machine Learning Approach (Victor Henrique Alves Ribeiro and Gilberto Reynoso Meza from the Pontifical Catholic University of Parana)
- Anomaly Detection for Drinking Water Quality via Deep BiLSTM Ensemble (Xingguo Chen, Fan Feng, Jikai Wu, and Wenyu Liu from the Nanjing University of Posts and Telecommunications and Nanjing University)
- Automatic vs. Manual Feature Engineering for Anomaly Detection of Drinking-Water Quality (Valerie Aenne Nicola Fehst from idatase GmbH)

Official webpage:

http://www.spotseven.de/gecco/gecco-challenge/gecco-challenge-2018/

Performance rates on frequently reported health care quality measures in the CMS Medicaid/CHIP Child and Adult Core Sets, for FFY 2018 reporting.

Source: Mathematica analysis of MACPro and Form CMS-416 reports for the FFY 2018 reporting cycle. For more information, see the Children's Health Care Quality Measures and Adult Health Care Quality Measures webpages.

This product was developed as part of the project supported by the grant from and the National Oceanic and Atmospheric Administration’s Ocean Acidification Program under award NA18OAR0170430 to the Virginia Institute of Marine Science. The data product consists of water quality data for tidal 98 stations for 1984–2018. The source data used to generate this product were downloaded from the Chesapeake Bay Program’s (CBP) data hub. Out of the total of 255 monitoring stations in the Tidal Monitoring Program, we selected 98 with the long monitoring record (30 years or longer). The following variables were downloaded from the data hub at the native temporal and vertical resolution (between one and four cruises per month and approximately 10 depth levels sampled between 0 and 37 m) for 1984–2018: water temperature (T), salinity (S), pH, total alkalinity (TA), dissolved oxygen (DO) , and chlorophyll (Chl). All pH data prior to 1998 were removed because of the data quality concerns (Herrmann et al., 2020). Briefly, we found a dramatic difference in long-term trends between stations measured by institutions in the state of Virginia and stations measured by the state of Maryland, particularly from late spring to early fall. The boundary between the station groups runs east–west within the mesohaline portion of the bay, where the Potomac River estuary intersects the mainstem bay. The boundary separates strong negative linear trends to the south (Virginia stations) from neutral and weakly positive linear trends to the north (Maryland stations). For all variables, data entries marked with CBP’s “Problem” and “Qualifier” flags were removed. Additionally, all variables were scanned for extreme outliers: for each variable, data from all stations, depths, and times were combined into a single composite sample for which the 75th and 25th percentiles (i.e., the upper and lower quantiles) and the interquartile range (the difference between the upper and lower quantiles) were calculated. Extreme outliers were defined as the values falling outside of a certain number (censoring criterion) of interquartile ranges from the upper and lower quantiles.

A list of schools receiving Quality Reviews during the 2018-19 school year

This statistic displays the results from a survey asking respondents in England to rate their satisfaction with the quality of healthcare services received from traditional and non-traditional care**. ** percent of respondents are satisfied with the effectiveness of their treatment from traditional healthcare, compared to ** percent satisfied with non-traditional care.

The California Water Quality Status Report is an annual data-driven snapshot of the Water Board’s water quality and ecosystem data. This second edition of the report is organized around the watershed from land to sea. Each theme-specific story includes a brief background, a data analysis summary, an overview of management actions, and access to the raw data.

View the 2018 California Water Quality Status Report.

• Data for Fig. 8 Landscape Constraints on Stream Biological Integrity in the San Gabriel River Watershed can be downloaded from Zenodo.
• Data for Fig. 13 HAB Incident Reports Map can be downloaded from the California Open Data Portal.

For more information please contact the Office of Information Management and Analysis (OIMA).

This dataset includes Medicaid Managed Care, Commercial HMO, and Commercial PPO performance data from the Quality Assurance Reporting Requirements (QARR) by member demographic characteristics. QARR is largely based on measures of quality developed and published by the National Committee for Quality Assurance (NCQA) Healthcare Effectiveness Data and Information Set (HEDIS®). Plans are required to submit quality performance data each year. Demographic information analyzed in this report includes members’ sex, age, race/ethnicity, Medicaid aid category, cash assistance status, behavioral health conditions including serious mental illness (SMI) and substance use disorder (SUD), payer status, and region of residence. Measuring the quality of care, and the ability to measure disparities in care is an important first step to a better understanding of the underlying factors that drive differences in care among certain populations within Medicaid Managed Care, Commercial HMO, and Commercial PPO.

These data are published annually for Medicaid Managed Care in the Health Care Disparities in New York State Report and on the NYSDOH website: https://www.health.ny.gov/health_care/managed_care/reports/

This survey shows the share of customers in the U.S. and worldwide by their opinion about customer service getting better in 2018. During the survey, ** percent of respondents from the United States stated that they think customer service is getting better.

Monitoring air quality Final Report 2018

This statistic shows the results of a survey conducted from February to June 2018 among adult Americans on their preferred men's athletic shoe brands. The results were sorted by recent purchases based on quality. During the survey, **** percent of respondents said they prefer Nike based on higher quality; **** percent of respondents stated they prefer Nike not based on higher quality.

This statistic shows the satisfaction with the quality of the health care system in the U.S. among millennials from 2013 to 2018. In 2018, ** percent of millennials were very satisfied with the health care system in the United States, while * percent were not very satisfied.

This dataset tracks the updates made on the dataset "2018 Child and Adult Health Care Quality Measures" as a repository for previous versions of the data and metadata.

The table Water Quality 2018 is part of the dataset Denver Water Quality 2011-2021, available at https://redivis.com/datasets/8vw2-75193933d. It contains 8684 rows across 16 variables.

This 14 year hourly air quality reanalysis dataset is generated through daily assimilation of the Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) and the Measurement of Pollution in the Troposphere (MOPITT) carbon monoxide (CO) retrievals in the Community Multiscale Air Quality Model (CMAQ) from 01 Jan 2005 to 31 Dec 2018. The production of this air quality reanalysis is funded by the Atmospheric Composition Modeling and Analysis Program (ACMAP) under the project entitled “Quantification and attribution of past (2005-2018) air quality trends over the Contiguous United States (CONUS) via assimilation of NASA atmospheric composition”. This data can be used to (1) develop products and metrics for assessing the long-term impact of air pollution on public health, agriculture, and economy as well as for quantifying air quality changes in unmonitored areas and benefits of emission control policies.

This dataset shows water quality monitoring and assessment of Trophic Status carried out on Irish Coastal Waters for the Reporting period 2018-2020.

This map shows the Habitat Quality Ecosystem Service supply potential in % for the year 2018. It considers the habitat quality and rarity as proxies to represent the biodiversity of a landscape.

The normalization method adopts the min-max normalization.

Spatial resolution: 100m

CTD depth profiles from a dataset supporting a baseline assessment of marine water quality around Vanuatu, South Pacific. As part of the Commonwealth Marine Economies Programme, water quality measurements were collected over three years in the coastal waters around the island of Efate, and on one occasion around the island of Tanna. Observations focus on Port Vila (Efate), which is the main urbanised area on the Island. Parameters included are: salinity, temperature, turbidity, light attenuation, dissolved oxygen, and chlorophyll (fluorescence) which represent part of a larger research program on water quality, human health and habitat mapping.

Associated data can be found here: Devlin et al (2020). Vanuatu Water Quality Dataset - 2016-2018. Cefas, UK. V1. doi: https://doi.org/10.14466/CefasDataHub.107_

.. _https://doi.org/10.14466/cefasdatahub.107: https://doi.org/10.14466/CefasDataHub.107